Front Cover -- The Spectral Analysis of Time Series -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Preface to the Second Edition -- Chapter 1. Preliminaries -- 1.1 Introduction -- 1.2 Time Series and Spectra -- 1.3 Summary of Vector Space Geometry -- 1.4 Some Probability Notations and Properties -- Chapter 2. Models for Spectral Analysis-The Univariate Case -- 2.1 Introduction -- 2.2 The Wiener Theory of Spectral Analysis -- 2.3 Stationary and Weakly Stationary Stochastic Processes -- 2.4 The Spectral Representation for Weakly Stationary Stochastic Processes-A Special Case -- 2.5 The General Spectral Representation for Weakly Stationary Processes -- 2.6 The Discrete and Continuous Components of the Process -- 2.7 Physical Realization of the Different Kinds of Spectra -- 2.8 The Real Spectral Representation -- 2.9 Ergodicity and the Connection between the Wiener and Stationary Process Theories -- 2.10 Statistical Estimation of the Autocovariance and the Mean Ergodic Theorem -- Appendix to Chapter 2 -- Chapter 3. Sampling, Aliasing, and Discrete-Time Models -- 3.1 Introduction -- 3.2 Sampling and the Aliasing Problem -- 3.3 The Spectral Model for Discrete-Time Series -- Chapter 4. Linear Filters-General Properties with Applications to Continuous-Time Processes -- 4.1 Introduction -- 4.2 Linear Filters -- 4.3 Combining Linear Filters -- 4.4 Inverting Linear Filters -- 4.5 Nonstationary Processes Generated by Time Varying Linear Filters -- Appendix to Chapter 4 -- Chapter 5. Multivariate Spectral Models and Their Applications -- 5.1 Introduction -- 5.2 The Spectrum of a Multivariate Time Series-Wiener Theory -- 5.3 Multivariate Weakly Stationary Stochastic Processes -- 5.4 Linear Filters for Multivariate Time Series -- 5.5 The Bivariate Spectral Parameters, Their Intepretations and Uses.

5.6 The Multivariate Spectral Parameters, Their Interpretations and Uses -- Appendix to Chapter 5 -- Chapter 6. Digital Filters -- 6.1 Introduction -- 6.2 General Properties of Digital Filters -- 6.3 The Effect of Finite Data Length -- 6.4 Digital Filters with Finitely Many Nonzero Weights -- 6.5 Digital Filters Obtained by Combining Simple Filters -- 6.6 Filters with Gapped Weights and Results Concerning the Filtering of Series with Polynomial Trends -- Appendix to Chapter 6 -- Chapter 7. Finite Parameter Models, Linear Prediction, and Real-Time Filtering -- 7.1 Introduction -- 7.2 Moving Averages -- 7.3 Autoregressive Processes -- 7.4 The Linear Prediction Problem -- 7.5 Mixed Autoregressive-Moving Average Processes and Recursive Prediction -- 7.6 Linear Filtering in Real Time -- Appendix to Chapter 7 -- Chapter 8. The Distribution Theory of Spectral Estimates with Applications to Statistical Inference -- 8.1 Introduction -- 8.2 Distribution of the Finite Fourier Transform and the Periodogram -- 8.3 Distribution Theory for Univariate Spectral Estimators -- 8.4 Distribution Theory for Multivariate Spectral Estimators with Applications to Statistical Inference -- Appendix to Chapter 8 -- Chapter 9. Sampling Properties of Spectral Estimates, Experimental Design, and Spectral Computations -- 9.1 Introduction -- 9.2 Properties of Spectral Estimators and the Selection of Spectral Windows -- 9.3 Experimental Design -- 9.4 Methods for Computing Spectral Estimators -- 9.5 Data Processing Problems and Techniques -- Appendix to Chapter 9 -- References -- Index -- Probability and Mathematical Statistics.